Legal claims defining the scope of protection. Each claim is shown in both the original legal language and a plain English translation.
1. A computer-implemented method comprising: configuring, by a computing device, a stochastic simulation scenario, wherein the stochastic simulation scenario is randomized and digital, includes hardware-in-the-loop architecture associated with the stochastic simulation scenario, and includes one or more variables without a complete probability distribution, wherein the one or more variables without a complete probability distribution includes human activity with an unknowable probability distribution; executing the stochastic simulation scenario to generate one or more results of the stochastic simulation scenario; and optimizing at least a portion of the one or more variables without the probability distribution using one or more optimization metrics on the one or more results of the stochastic simulation scenario.
This invention relates to stochastic simulation methods for optimizing systems involving human activity and other variables with incomplete probability distributions. The method addresses the challenge of modeling and optimizing complex systems where certain variables, particularly human behavior, lack complete probabilistic characterization. The approach integrates hardware-in-the-loop (HIL) architecture into a randomized digital simulation framework, allowing real-world hardware components to interact with simulated elements. The simulation scenario is configured with variables that cannot be fully described by probability distributions, such as human actions with inherently unpredictable patterns. The system executes the stochastic simulation, generating results that are then analyzed using optimization metrics. These metrics guide adjustments to the variables, refining the simulation to improve outcomes despite the uncertainty in human behavior and other unquantified factors. The method enables iterative optimization of systems where traditional probabilistic modeling is insufficient, providing a practical approach to handling real-world complexity in simulations.
2. The computer-implemented method of claim 1 wherein the stochastic simulation scenario includes at least one of: a land vehicle, an air vehicle, and a water vehicle; one or more fixed base articulated robot arms; and one or more mobile articulated robot arms mounted on at least one of the land vehicle, the air vehicle, and the water vehicle.
This invention relates to stochastic simulation scenarios involving vehicles and robotic systems. The technology addresses the need for realistic and dynamic simulations of autonomous or semi-autonomous systems operating in complex environments. The method simulates interactions between vehicles and robotic systems, including land, air, and water vehicles, as well as fixed and mobile articulated robot arms. The simulation models the behavior of these systems under uncertain or variable conditions, allowing for testing and validation of control algorithms, navigation systems, and coordination strategies. The scenarios may involve single or multiple vehicles, each equipped with one or more articulated robot arms, enabling the simulation of tasks such as manipulation, object handling, and environmental interaction. The stochastic nature of the simulation accounts for real-world uncertainties, such as sensor noise, environmental dynamics, and system failures, to improve the robustness of the simulated systems. This approach is useful in fields like autonomous transportation, industrial automation, and defense, where reliable performance under unpredictable conditions is critical. The simulation framework supports the development and refinement of control policies, ensuring that the systems can operate effectively in diverse and challenging scenarios.
3. The computer-implemented method of claim 1 wherein the stochastic simulation scenario includes an autonomous convoy.
The invention relates to a computer-implemented method for generating and analyzing stochastic simulation scenarios in a transportation or logistics system. The core problem addressed is the need to model and evaluate complex, unpredictable operational conditions, such as those encountered in autonomous vehicle fleets or robotic convoy systems, where traditional deterministic simulations fall short. The method involves creating a stochastic simulation scenario that includes an autonomous convoy, which is a group of self-driving vehicles operating in coordination. This convoy is modeled to account for random variables and uncertainties inherent in real-world operations, such as sensor noise, communication delays, environmental factors, or unpredictable human behavior. The simulation dynamically adjusts parameters like vehicle spacing, speed, and reaction times to reflect probabilistic outcomes rather than fixed values. The autonomous convoy is further integrated with other system components, such as traffic management algorithms, obstacle detection systems, or decision-making frameworks, to assess performance under varying conditions. The method may also include validation steps to compare simulation results against real-world data or predefined benchmarks, ensuring the accuracy and reliability of the stochastic model. By incorporating an autonomous convoy into the simulation, the invention enables more realistic testing of safety protocols, efficiency metrics, and failure recovery strategies in autonomous driving systems, particularly in high-risk or edge-case scenarios that are difficult to replicate in physical testing environments.
4. The computer-implemented method of claim 1 wherein the one or more optimization metrics includes a threshold measure of variance.
This invention relates to computer-implemented methods for optimizing systems or processes by evaluating one or more optimization metrics, with a focus on controlling variance. The method addresses the challenge of ensuring system performance remains within acceptable bounds while improving efficiency or other desired outcomes. The core technique involves analyzing performance data to determine whether the variance of key metrics exceeds a predefined threshold. If the variance is too high, the system adjusts parameters or operations to reduce variability, ensuring stability and reliability. The method may also incorporate additional optimization metrics beyond variance, such as speed, cost, or resource utilization, to achieve a balanced improvement. By dynamically monitoring and adjusting based on variance thresholds, the system avoids extreme fluctuations that could degrade performance or cause failures. This approach is particularly useful in applications where consistency is critical, such as manufacturing processes, financial systems, or real-time control systems. The method may be implemented in software, hardware, or a combination of both, and can be applied to various industries where performance stability is a priority.
5. The computer-implemented method of claim 1 wherein the stochastic simulation scenario uses Monte Carlo analysis.
A computer-implemented method performs stochastic simulation scenarios to analyze and optimize complex systems, particularly in fields like finance, engineering, or operations research. The method addresses the challenge of modeling uncertainty and variability in dynamic systems where deterministic approaches are insufficient. By employing stochastic simulation, the method generates probabilistic outcomes to assess risk, performance, or other key metrics under uncertain conditions. The method involves defining system parameters, generating random inputs based on probability distributions, and executing simulations to produce output data. The stochastic nature of the simulation allows for the exploration of multiple possible outcomes, providing a more comprehensive understanding of system behavior compared to deterministic models. In one implementation, the stochastic simulation scenario specifically uses Monte Carlo analysis, a statistical technique that relies on repeated random sampling to obtain numerical results. Monte Carlo methods are particularly useful for modeling systems with high dimensionality or complex interdependencies, where analytical solutions are impractical. The method may include steps such as defining probability distributions for input variables, running multiple simulations with different random inputs, and aggregating results to derive statistical measures like mean, variance, or confidence intervals. The method may also incorporate optimization techniques to adjust system parameters based on simulation results, aiming to improve performance or mitigate risks. By leveraging stochastic simulation, the method enables decision-makers to evaluate trade-offs and make informed choices in uncertain environments.
6. The computer-implemented method of claim 1 wherein the stochastic simulation scenario includes at least one of an orbit, an asteroid, a comet, a planet, and a moon.
A computer-implemented method for generating stochastic simulation scenarios involving celestial bodies. The invention addresses the need to model dynamic astronomical environments by simulating the behavior and interactions of various celestial objects. The method involves creating probabilistic scenarios that include at least one of the following: an orbit, an asteroid, a comet, a planet, or a moon. These scenarios are designed to account for uncertainties in orbital mechanics, trajectories, and interactions, enabling more accurate predictions of celestial movements and potential collisions. The simulation framework allows for the integration of real-time data and historical observations to refine probabilistic models, enhancing the reliability of the generated scenarios. By incorporating stochastic elements, the method provides a robust tool for astronomers, space agencies, and researchers to assess risks, plan missions, and study the long-term stability of celestial systems. The approach ensures flexibility in scenario generation, accommodating a wide range of celestial objects and their dynamic behaviors.
7. The computer-implemented method of claim 1 wherein optimizing at least a portion of the one or more variables includes optimizing over multiple parameters to enable competing objective functions.
The invention relates to a computer-implemented optimization method for managing multiple competing objectives in a system. The core problem addressed is the need to balance and optimize several parameters simultaneously, where these parameters may have conflicting goals or constraints. The method involves adjusting one or more variables within the system to achieve the best possible outcome across these competing objectives, rather than focusing on a single metric. This approach allows for a more nuanced and flexible decision-making process, where trade-offs between different objectives can be systematically evaluated and resolved. The optimization process may involve iterative adjustments, where the system evaluates the impact of changes on each objective and refines the variables to improve overall performance. The method is particularly useful in scenarios where resources are limited, or where different stakeholders have varying priorities that must be reconciled. By enabling the optimization of multiple parameters in parallel, the invention provides a way to handle complex, real-world problems where simple single-objective optimization would be insufficient.
8. A non-transitory computer program product residing on a computer readable storage medium having a plurality of instructions stored thereon which, when executed across one or more processors, causes at least a portion of the one or more processors to perform operations comprising: configuring a stochastic simulation scenario, wherein the stochastic simulation scenario is randomized and digital, includes hardware-in-the-loop architecture associated with the stochastic simulation scenario, and includes one or more variables without a complete probability distribution, wherein the one or more variables without a complete probability distribution includes human activity with an unknowable probability distribution; executing the stochastic simulation scenario to generate one or more results of the stochastic simulation scenario; and optimizing at least a portion of the one or more variables without the probability distribution using one or more optimization metrics on the one or more results of the stochastic simulation scenario.
The invention relates to a computer-implemented method for conducting stochastic simulations with hardware-in-the-loop architecture, addressing scenarios where certain variables lack complete probability distributions, particularly human activities with inherently unpredictable behavior. The system configures a randomized digital simulation environment that integrates real hardware components, allowing for testing under controlled yet unpredictable conditions. The simulation includes variables for which full probabilistic data is unavailable, such as human-driven factors, and executes the scenario to produce results. These results are then analyzed using optimization metrics to refine and improve the uncertain variables, enabling better decision-making in complex, real-world systems where traditional probabilistic modeling falls short. The approach leverages hardware-in-the-loop testing to validate performance in a physically accurate context while accounting for unpredictable human interactions. The solution is implemented as a non-transitory computer program stored on a readable medium, executable across one or more processors to perform these operations.
9. The computer program product of claim 8 wherein the stochastic simulation scenario includes at least one of: a land vehicle, an air vehicle, and a water vehicle; one or more fixed base articulated robot arms; and one or more mobile articulated robot arms mounted on at least one of the land vehicle, the air vehicle, and the water vehicle.
The invention relates to a computer program product designed for stochastic simulation scenarios involving robotic systems and vehicles. The technology domain focuses on simulating interactions between robotic arms and various types of vehicles to evaluate performance, safety, or operational efficiency in dynamic environments. The computer program product enables the simulation of at least one of the following vehicle types: land vehicles, air vehicles, or water vehicles. Additionally, it supports the simulation of one or more fixed base articulated robot arms, which are stationary robotic manipulators typically used in industrial or manufacturing settings. The product also facilitates the simulation of one or more mobile articulated robot arms, which are mounted on at least one of the supported vehicle types. These mobile robotic arms can be deployed in diverse operational contexts, such as search and rescue, construction, or logistics, where mobility and adaptability are critical. By integrating these robotic systems with vehicle simulations, the invention provides a comprehensive tool for testing and optimizing robotic behaviors in complex, real-world scenarios. The stochastic nature of the simulation allows for the evaluation of system performance under varying conditions, including unpredictable environmental factors or operational uncertainties. This capability is particularly valuable for industries such as autonomous systems, robotics, and transportation, where safety and reliability are paramount.
10. The computer program product of claim 8 wherein the stochastic simulation scenario includes an autonomous convoy.
The invention relates to a computer program product designed for stochastic simulation scenarios, specifically focusing on modeling an autonomous convoy. The system enables the simulation of multiple autonomous vehicles operating in a coordinated convoy formation, where each vehicle follows predefined or dynamically adjusted paths while maintaining safe distances and communication protocols. The simulation accounts for various environmental factors, such as terrain, weather, and potential obstacles, to evaluate the convoy's performance under different conditions. The program product likely integrates with existing simulation frameworks to provide realistic vehicle dynamics, sensor inputs, and decision-making algorithms for the autonomous convoy. By simulating such scenarios, the invention aims to assess the reliability, safety, and efficiency of autonomous convoy operations before real-world deployment, reducing risks and optimizing operational parameters. The stochastic nature of the simulation allows for probabilistic modeling of unpredictable events, such as mechanical failures or sudden environmental changes, ensuring robust testing of the convoy's adaptive capabilities.
11. The computer program product of claim 8 wherein the one or more optimization metrics includes a threshold measure of variance.
A system and method for optimizing data processing operations in a distributed computing environment addresses inefficiencies in resource allocation and performance bottlenecks. The invention focuses on improving computational efficiency by dynamically adjusting processing parameters based on optimization metrics, including a threshold measure of variance. The system monitors data processing tasks across distributed nodes, evaluates performance metrics such as execution time, resource utilization, and variance in task completion times, and adjusts workload distribution or processing strategies to minimize deviations from optimal performance. The threshold measure of variance ensures that adjustments are made only when performance fluctuations exceed predefined limits, preventing unnecessary reconfigurations. By incorporating variance thresholds, the system balances responsiveness to performance changes with stability, avoiding frequent adjustments that could disrupt ongoing operations. The invention is particularly useful in large-scale data processing environments where maintaining consistent performance across distributed systems is challenging. The optimization process involves real-time analysis of task execution patterns, comparison against variance thresholds, and automated reallocation of resources or tasks to maintain efficiency. This approach enhances overall system throughput and reliability while reducing operational overhead.
12. The computer program product of claim 8 wherein the stochastic simulation scenario uses Monte Carlo analysis.
The invention relates to a computer-implemented method for evaluating the performance or reliability of a system, process, or financial model through stochastic simulation. The core technique involves generating multiple random scenarios to assess potential outcomes under uncertainty. Specifically, the method employs Monte Carlo analysis, a statistical sampling technique that runs numerous simulations with varied input parameters to produce a distribution of possible results. This approach quantifies risk, variability, or uncertainty by calculating probabilities or confidence intervals for different outcomes. The simulation scenarios are designed to model real-world conditions where input variables follow probabilistic distributions, allowing for a comprehensive evaluation of system behavior under diverse conditions. The Monte Carlo method is particularly useful in fields such as finance, engineering, project management, and operations research, where deterministic models fail to capture the inherent randomness of the environment. By leveraging random sampling and statistical aggregation, the technique provides insights into the likelihood of extreme events, expected performance, and sensitivity to input variations. The computer program product implementing this method includes algorithms for generating random inputs, running simulations, and analyzing the aggregated results to derive meaningful metrics or predictions.
13. The computer program product of claim 8 wherein the stochastic simulation scenario includes at least one of an orbit, an asteroid, a comet, a planet, and a moon.
The invention relates to a computer program product designed for stochastic simulation scenarios involving celestial bodies. The program generates and evaluates random simulation scenarios that include at least one of the following celestial objects: an orbit, an asteroid, a comet, a planet, or a moon. These scenarios are used to model and analyze dynamic interactions, trajectories, or other orbital mechanics-related phenomena in a probabilistic manner. The simulation framework likely incorporates randomness to account for uncertainties in initial conditions, environmental factors, or other variables affecting the behavior of celestial bodies. By including diverse celestial objects, the program enables comprehensive testing of orbital dynamics, collision risks, gravitational influences, or mission planning under varied and unpredictable conditions. The stochastic approach allows for the assessment of potential outcomes across a wide range of possible scenarios, enhancing the robustness of predictions or decision-making processes in space exploration, astronomy, or related fields. The program product may integrate with existing simulation tools or operate as a standalone application, providing flexibility in deployment and use.
14. The computer program product of claim 8 wherein optimizing at least a portion of the one or more variables includes optimizing over multiple parameters to enable competing objective functions.
The invention relates to a computer program product designed to optimize variables in a computational system by handling multiple competing objective functions through parameter adjustments. The system processes one or more variables, where optimization involves balancing or prioritizing different objectives that may conflict with each other. For example, in a multi-objective optimization scenario, the program adjusts parameters to find a solution that best satisfies multiple goals simultaneously, such as minimizing cost while maximizing efficiency or performance. This approach allows for dynamic decision-making where trade-offs between objectives are explicitly managed, rather than optimizing for a single metric. The method may involve iterative refinement, where parameters are tuned based on feedback from the competing objectives, ensuring that the final solution reflects a balanced outcome. Potential applications include resource allocation, scheduling, or design optimization where multiple performance criteria must be considered. The program product likely integrates with existing computational frameworks, providing a mechanism to evaluate and adjust variables in real-time or batch processing modes. The core technical contribution lies in the ability to handle conflicting objectives without requiring a single predefined priority, enabling more flexible and adaptive optimization strategies.
15. A computing system including one or more processors and one or more memories configured to perform operations comprising: configuring a stochastic simulation scenario, wherein the stochastic simulation scenario is randomized and digital and includes one or more variables without a complete probability distribution, wherein the one or more variables without a complete probability distribution includes human activity with an unknowable probability distribution; executing the stochastic simulation scenario to generate one or more results of the stochastic simulation scenario; and optimizing at least a portion of the one or more variables without the probability distribution using one or more optimization metrics on the one or more results of the stochastic simulation scenario, wherein optimizing includes an optimization algorithm held within an XML-specified class.
The computing system is designed for stochastic simulation and optimization, particularly in scenarios where variables lack complete probability distributions, such as human activity with inherently unknowable probabilities. The system configures a digital, randomized simulation scenario that includes these uncertain variables. The simulation is executed to generate results, which are then analyzed and optimized using predefined metrics. The optimization process employs an algorithm defined within an XML-specified class, allowing for flexible and structured implementation. This approach enables decision-making in complex systems where traditional probabilistic modeling is infeasible due to incomplete or unknowable distributions. The system leverages computational methods to iteratively refine variables, improving outcomes based on observed simulation results. The use of XML for algorithm specification ensures modularity and adaptability across different simulation contexts. This technology is particularly useful in fields like urban planning, healthcare, or logistics, where human behavior introduces significant uncertainty. The system's ability to handle incomplete data and optimize under uncertainty distinguishes it from deterministic or fully probabilistic models.
16. The computing system of claim 15 wherein the stochastic simulation scenario includes hardware-in-the-loop architecture associated with the stochastic simulation scenario.
A computing system for executing a stochastic simulation scenario that incorporates a hardware-in-the-loop (HIL) architecture. The system integrates real hardware components into the simulation environment, allowing physical devices or subsystems to interact with the simulated model in real time. This hybrid approach enables testing of control algorithms, sensors, or embedded systems under realistic conditions without requiring a fully operational physical prototype. The HIL architecture typically involves connecting the hardware under test to a simulation platform via interfaces such as analog-to-digital converters, digital input/output modules, or communication buses. The stochastic simulation scenario generates variable conditions, such as environmental disturbances or operational uncertainties, to evaluate system performance, robustness, and fault tolerance. By combining probabilistic modeling with real-world hardware interactions, the system enhances validation accuracy for applications in automotive, aerospace, industrial automation, or power systems. The architecture supports dynamic feedback loops where hardware responses influence simulation parameters, improving the fidelity of system behavior analysis.
17. The computing system of claim 15 wherein the stochastic simulation scenario includes at least one of: a land vehicle, an air vehicle, and a water vehicle; one or more fixed base articulated robot arms; and one or more mobile articulated robot arms mounted on at least one of the land vehicle, the air vehicle, and the water vehicle.
The invention relates to a computing system designed for stochastic simulation scenarios involving robotic and vehicular systems. The system simulates interactions between different types of vehicles and robotic arms to evaluate performance, safety, or operational efficiency in dynamic environments. The stochastic simulation includes at least one of the following: a land vehicle, an air vehicle, or a water vehicle. Additionally, the system incorporates one or more fixed base articulated robot arms, which are stationary robotic manipulators typically used for precise tasks in controlled settings. It also includes one or more mobile articulated robot arms, which are mounted on at least one of the land, air, or water vehicles, enabling robotic manipulation in mobile or remote locations. The computing system integrates these components to model complex scenarios where robotic arms interact with moving vehicles, such as in autonomous navigation, search and rescue operations, or industrial automation. The simulation accounts for uncertainties and variability in real-world conditions, providing a robust framework for testing and validating robotic and vehicular behaviors in combined operational environments.
18. The computing system of claim 15 wherein the stochastic simulation scenario includes an autonomous convoy.
The invention relates to computing systems for simulating autonomous vehicle operations, particularly in the context of autonomous convoys. The system addresses the challenge of accurately modeling and testing autonomous vehicle behaviors in complex, dynamic environments, such as military or logistics scenarios where multiple autonomous vehicles operate in coordinated groups. The computing system generates stochastic simulation scenarios that include autonomous convoys, where the convoy consists of multiple autonomous vehicles that navigate and interact with each other and the environment. The system simulates the convoy's movements, decision-making processes, and responses to dynamic conditions, such as obstacles, traffic, or communication disruptions. The simulation accounts for vehicle-to-vehicle communication, path planning, and collision avoidance to ensure realistic and reliable testing of autonomous convoy behaviors. The system may also incorporate environmental factors, such as weather or terrain, to further enhance the realism of the simulations. This approach enables developers to evaluate the performance and safety of autonomous convoy systems before real-world deployment, reducing risks and improving operational efficiency. The invention focuses on the stochastic nature of the scenarios, ensuring that simulations cover a wide range of possible real-world conditions.
19. The computing system of claim 15 wherein the one or more optimization metrics includes a threshold measure of variance.
The technology domain involves a computing system designed to optimize performance or efficiency based on one or more measurable criteria. The specific problem addressed is the need to evaluate and adjust system behavior by monitoring variability in key parameters, ensuring that deviations from desired performance levels are detected and managed. The system incorporates one or more optimization metrics, which are quantitative measures used to assess the effectiveness of system operations. Among these metrics, a threshold measure of variance is included, serving as a critical indicator of stability or consistency in system performance. This variance threshold allows the system to identify when operational conditions deviate beyond acceptable limits, triggering corrective actions or adjustments to maintain optimal functionality. By setting predefined limits on acceptable variability, the system can proactively respond to fluctuations, ensuring reliable and predictable performance. This approach is particularly useful in environments where maintaining consistent output or behavior is essential, such as in real-time data processing, automated control systems, or resource allocation frameworks. The variance threshold metric enables the system to dynamically adapt, balancing efficiency with stability while minimizing disruptions caused by unpredictable variations.
20. The computing system of claim 15 wherein the stochastic simulation scenario uses Monte Carlo analysis.
The invention relates to a computing system designed for performing stochastic simulations to evaluate complex systems or processes under uncertainty. The system employs Monte Carlo analysis, a statistical method that uses repeated random sampling to obtain numerical results, to generate and analyze multiple possible scenarios. This approach allows for the assessment of risk, variability, and potential outcomes in dynamic environments where deterministic models may fall short. By simulating a wide range of possible inputs and conditions, the system provides probabilistic insights into system behavior, decision-making, and performance metrics. The Monte Carlo analysis is integrated into the simulation framework to handle large-scale, high-dimensional problems efficiently, ensuring robust and reliable predictions. The computing system may include components for data input, scenario generation, simulation execution, and result aggregation, all optimized for handling the computational demands of stochastic modeling. This method is particularly useful in fields such as finance, engineering, healthcare, and operations research, where understanding uncertainty and variability is critical for informed decision-making.
21. The computing system of claim 15 wherein the stochastic simulation scenario includes at least one of an orbit, an asteroid, a comet, a planet, and a moon.
This invention relates to a computing system for simulating celestial mechanics scenarios, particularly for modeling the motion and interactions of celestial bodies. The system addresses the challenge of accurately predicting the trajectories and behaviors of objects in space, such as orbits, asteroids, comets, planets, and moons, under the influence of gravitational forces and other dynamic factors. The computing system generates stochastic simulation scenarios that incorporate these celestial bodies, allowing for probabilistic modeling of their movements and interactions. The system includes a simulation engine that processes input parameters, such as initial positions, velocities, and gravitational constants, to compute the trajectories of the celestial bodies over time. It also includes a visualization module that displays the simulation results, enabling users to observe the dynamic behavior of the simulated objects. The system may further include a user interface for defining simulation parameters and adjusting the simulation settings. The stochastic nature of the simulations allows for the modeling of uncertainties and variations in the behavior of celestial bodies, providing more realistic and comprehensive predictions of their motion. This technology is useful in fields such as astronomy, space exploration, and planetary science, where accurate modeling of celestial mechanics is essential.
22. The computing system of claim 15 wherein optimizing at least a portion of the one or more variables includes optimizing over multiple parameters to enable competing objective functions.
The invention relates to a computing system designed to optimize variables within a computational model by balancing multiple competing objective functions. The system addresses the challenge of multi-objective optimization, where different parameters must be adjusted simultaneously to achieve optimal performance across conflicting goals. For example, in resource allocation, the system may need to minimize cost while maximizing efficiency, requiring trade-offs between these objectives. The computing system processes one or more variables by evaluating their impact across multiple parameters. It employs an optimization algorithm that considers the interdependencies between these parameters to determine the best possible configuration. The system dynamically adjusts the variables to satisfy competing objective functions, ensuring that no single objective is prioritized at the expense of others. This approach is particularly useful in scenarios where trade-offs are necessary, such as in machine learning, logistics, or financial modeling. By optimizing over multiple parameters, the system provides a balanced solution that accounts for the varying priorities of different objectives. The method ensures that the computed variables meet the desired criteria without favoring one objective over another, leading to a more efficient and effective outcome. The system's ability to handle competing objectives makes it suitable for complex real-world problems where multiple goals must be simultaneously optimized.
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April 28, 2020
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